PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variable Scaling for Time Series Prediction: Application to the ESTSP'07 and the NN3 Forecasting Competitions
Elia Liitiäinen and Amaury Lendasse
In: Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 12-17 Aug. 2007, Orlando, FL, USA.


In this paper, variable selection and variable scaling are used in order to select the best regressor for the problem of time series prediction. Direct prediction methodology is used instead of the classic recursive methodology. Least Squares Support Vector Machines (LS-SVM) and K-NN approximator are used in order to avoid local minimal in the training phase of the model. The global methodology is applied to the ESTSP'07 competition dataset [1] and the dataset B of the NN3 Forecasting Competition [2].

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3739
Deposited By:Amaury Lendasse
Deposited On:15 February 2008